Application of Computer Vision and Machine Learning for Digitized Herbarium Specimens: A Systematic Literature Review

• URL: http://arxiv.org/abs/2104.08732v1
• Date: Sun, 18 Apr 2021 06:08:51 GMT
• Title: Application of Computer Vision and Machine Learning for Digitized Herbarium Specimens: A Systematic Literature Review
• Authors: Burhan Rashid Hussein, Owais Ahmed Malik, Wee-Hong Ong, Johan Willem Frederik Slik
• Abstract summary: Herbarium contains treasures of millions of specimens which have been preserved for several years for scientific studies. digitization of these specimens is currently on going to facilitate easy access and sharing of its data to a wider scientific community. Online digital repositories such as IDigBio and GBIF have already accumulated millions of specimen images yet to be explored. This presents a perfect time to automate and speed up more novel discoveries using machine learning and computer vision.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Herbarium contains treasures of millions of specimens which have been preserved for several years for scientific studies. To speed up more scientific discoveries, a digitization of these specimens is currently on going to facilitate easy access and sharing of its data to a wider scientific community. Online digital repositories such as IDigBio and GBIF have already accumulated millions of specimen images yet to be explored. This presents a perfect time to automate and speed up more novel discoveries using machine learning and computer vision. In this study, a thorough analysis and comparison of more than 50 peer-reviewed studies which focus on application of computer vision and machine learning techniques to digitized herbarium specimen have been examined. The study categorizes different techniques and applications which have been commonly used and it also highlights existing challenges together with their possible solutions. It is our hope that the outcome of this study will serve as a strong foundation for beginners of the relevant field and will also shed more light for both computer science and ecology experts.

Related papers

• MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows [58.56005277371235]
  We introduce MASSW, a comprehensive text dataset on Multi-Aspect Summarization of ScientificAspects. MASSW includes more than 152,000 peer-reviewed publications from 17 leading computer science conferences spanning the past 50 years. We demonstrate the utility of MASSW through multiple novel machine-learning tasks that can be benchmarked using this new dataset.
  arXiv  Detail & Related papers  (2024-06-10T15:19:09Z)
• Gravitational cell detection and tracking in fluorescence microscopy data [0.18828620190012021]
  We present a novel approach based on gravitational force fields that can compete with, and potentially outperform modern machine learning models. This method includes detection, segmentation, and tracking elements, with the results demonstrated on a Cell Tracking Challenge dataset.
  arXiv  Detail & Related papers  (2023-12-06T14:08:05Z)
• Machine Learning for Leaf Disease Classification: Data, Techniques and Applications [14.73818032506552]
  In recent years, machine learning has been adopted for leaf disease classification in both academic research and industrial applications. This study will provide a survey in different aspects of the topic including data, techniques, and applications.
  arXiv  Detail & Related papers  (2023-10-19T06:21:21Z)
• Computer Vision on X-ray Data in Industrial Production and Security Applications: A survey [89.45221564651145]
  This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications. It covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets.
  arXiv  Detail & Related papers  (2022-11-10T13:37:36Z)
• Deep Learning to See: Towards New Foundations of Computer Vision [88.69805848302266]
  This book criticizes the supposed scientific progress in the field of computer vision. It proposes the investigation of vision within the framework of information-based laws of nature.
  arXiv  Detail & Related papers  (2022-06-30T15:20:36Z)
• Scientific Machine Learning Benchmarks [0.17205106391379021]
  The breakthrough in Deep Learning neural networks has transformed the use of AI and machine learning technologies for the analysis of very large experimental datasets. Identifying the most appropriate machine learning algorithm for the analysis of any given scientific dataset is still a challenge for scientists. We describe our approach to the development of scientific machine learning benchmarks and review other approaches to benchmarking scientific machine learning.
  arXiv  Detail & Related papers  (2021-10-25T10:05:11Z)
• Ultrafast Focus Detection for Automated Microscopy [0.0]
  We present a fast out-of-focus detection algorithm for electron microscopy images collected serially. Our technique, Multi-scale Histologic Feature Detection, adapts classical computer vision techniques and is based on detecting various fine-grained histologic features. Tests are performed that demonstrate near-real-time detection of out-of-focus conditions.
  arXiv  Detail & Related papers  (2021-08-26T22:24:41Z)
• Ten Quick Tips for Deep Learning in Biology [116.78436313026478]
  Machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Deep learning has become its own subfield of machine learning. In the context of biological research, deep learning has been increasingly used to derive novel insights from high-dimensional biological data.
  arXiv  Detail & Related papers  (2021-05-29T21:02:44Z)
• Machine Learning Applications for Therapeutic Tasks with Genomics Data [49.98249191161107]
  We review the literature on machine learning applications for genomics through the lens of therapeutic development. We identify twenty-two machine learning in genomics applications across the entire therapeutics pipeline. We pinpoint seven important challenges in this field with opportunities for expansion and impact.
  arXiv  Detail & Related papers  (2021-05-03T21:20:20Z)
• Self-Supervised Learning for Visual Summary Identification in Scientific Publications [21.26121265868308]
  We create a new benchmark dataset for selecting figures to serve as visual summaries of publications based on their abstracts. We develop a self-supervised learning approach, based on matching of inline references to figures with figure captions. Experiments in both biomedical and computer science domains show that our model is able to outperform the state of the art.
  arXiv  Detail & Related papers  (2020-12-21T09:48:58Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.